5 research outputs found
A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling
While diffusion MRI has been extremely promising in the study of MTBI,
identifying patients with recent MTBI remains a challenge. The literature is
mixed with regard to localizing injury in these patients, however, gray matter
such as the thalamus and white matter including the corpus callosum and frontal
deep white matter have been repeatedly implicated as areas at high risk for
injury. The purpose of this study is to develop a machine learning framework to
classify MTBI patients and controls using features derived from multi-shell
diffusion MRI in the thalamus, frontal white matter and corpus callosum
Identifying Mild Traumatic Brain Injury Patients From MR Images Using Bag of Visual Words
Mild traumatic brain injury (mTBI) is a growing public health problem with an
estimated incidence of one million people annually in US. Neurocognitive tests
are used to both assess the patient condition and to monitor the patient
progress. This work aims to directly use MR images taken shortly after injury
to detect whether a patient suffers from mTBI, by incorporating machine
learning and computer vision techniques to learn features suitable
discriminating between mTBI and normal patients. We focus on 3 regions in
brain, and extract multiple patches from them, and use bag-of-visual-word
technique to represent each subject as a histogram of representative patterns
derived from patches from all training subjects. After extracting the features,
we use greedy forward feature selection, to choose a subset of features which
achieves highest accuracy. We show through experimental studies that BoW
features perform better than the simple mean value features which were used
previously
A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI
Mild traumatic brain injury is a growing public health problem with an
estimated incidence of over 1.7 million people annually in US. Diagnosis is
based on clinical history and symptoms, and accurate, concrete measures of
injury are lacking. This work aims to directly use diffusion MR images obtained
within one month of trauma to detect injury, by incorporating deep learning
techniques. To overcome the challenge due to limited training data, we describe
each brain region using the bag of word representation, which specifies the
distribution of representative patch patterns. We apply a convolutional
auto-encoder to learn the patch-level features, from overlapping image patches
extracted from the MR images, to learn features from diffusion MR images of
brain using an unsupervised approach. Our experimental results show that the
bag of word representation using patch level features learnt by the auto
encoder provides similar performance as that using the raw patch patterns, both
significantly outperform earlier work relying on the mean values of MR metrics
in selected brain regions.Comment: arXiv admin note: text overlap with arXiv:1710.0682
MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features
In this work, we propose bag of adversarial features (BAF) for identifying
mild traumatic brain injury (MTBI) patients from their diffusion magnetic
resonance images (MRI) (obtained within one month of injury) by incorporating
unsupervised feature learning techniques. MTBI is a growing public health
problem with an estimated incidence of over 1.7 million people annually in US.
Diagnosis is based on clinical history and symptoms, and accurate, concrete
measures of injury are lacking. Unlike most of previous works, which use
hand-crafted features extracted from different parts of brain for MTBI
classification, we employ feature learning algorithms to learn more
discriminative representation for this task. A major challenge in this field
thus far is the relatively small number of subjects available for training.
This makes it difficult to use an end-to-end convolutional neural network to
directly classify a subject from MR images. To overcome this challenge, we
first apply an adversarial auto-encoder (with convolutional structure) to learn
patch-level features, from overlapping image patches extracted from different
brain regions. We then aggregate these features through a bag-of-word approach.
We perform an extensive experimental study on a dataset of 227 subjects
(including 109 MTBI patients, and 118 age and sex matched healthy controls),
and compare the bag-of-deep-features with several previous approaches. Our
experimental results show that the BAF significantly outperforms earlier works
relying on the mean values of MR metrics in selected brain regions.Comment: IEEE Transactions on Medical Imagin
Image Segmentation Using Subspace Representation and Sparse Decomposition
Image foreground extraction is a classical problem in image processing and
vision, with a large range of applications. In this dissertation, we focus on
the extraction of text and graphics in mixed-content images, and design novel
approaches for various aspects of this problem.
We first propose a sparse decomposition framework, which models the
background by a subspace containing smooth basis vectors, and foreground as a
sparse and connected component. We then formulate an optimization framework to
solve this problem, by adding suitable regularizations to the cost function to
promote the desired characteristics of each component. We present two
techniques to solve the proposed optimization problem, one based on alternating
direction method of multipliers (ADMM), and the other one based on robust
regression. Promising results are obtained for screen content image
segmentation using the proposed algorithm.
We then propose a robust subspace learning algorithm for the representation
of the background component using training images that could contain both
background and foreground components, as well as noise. With the learnt
subspace for the background, we can further improve the segmentation results,
compared to using a fixed subspace. Lastly, we investigate a different class of
signal/image decomposition problem, where only one signal component is active
at each signal element. In this case, besides estimating each component, we
need to find their supports, which can be specified by a binary mask. We
propose a mixed-integer programming problem, that jointly estimates the two
components and their supports through an alternating optimization scheme. We
show the application of this algorithm on various problems, including image
segmentation, video motion segmentation, and also separation of text from
textured images.Comment: PhD Dissertation, NYU, 201